State-of-the-art comparison. We compared our solution with the results obtained in [1]}, where a centralized detection-based counting approach has been presented. It is based on the original version of Mask R-CNN having the ResNet101 model as features extractor, which has been fine-tuned on a very small manually annotated subset of the CNRPark-EXT dataset, starting from the model pre-trained on the COCO dataset [2]}. Although this solution is computationally expensive and unsuitable for edge devices, it represents a direct comparison in terms of counting on the same dataset. We also compared our technique against the method proposed in [3]}, an approach for car parking occupancy detection based on mAlexNet, a deep cnn designed explicitly for smart cameras. This work represents an indirect method for counting cars in a parking lot, as the counting problem is cast as a classification problem: if a parking space is occupied, we increment the total number of cars. We illustrate the results in tab:resultsedgecounting, where we also report the performance obtained using the Mask R-CNN network without a preliminary fine-tuning on the CNRPark-EXT dataset. Our solution performed better than the other state-of-the-art considered methods, considering all three counting metrics. In particular, our approach outperformed the solution introduced in [1]}, despite the latter employing a more deep and powerful cnn, and it is designed to be used as a centralized-server solution. This is explained by the fact that in [1]}, the authors fine-tuned the cnn using a tiny dataset. Consequently, the algorithm overfits over the training data, and it cannot generalize over the test subset. It is also worthy of notice that our cnn also outperformed the mAlexNet network, even though the latter knows the exact location of the parking spaces. fig:examplecountingoutput shows some examples of images belonging to different cameras and different weather conditions together with the masks localizing the found vehicles.
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